refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data

TLDR

Reference intervals are essential for interpreting laboratory test results in medicine. The study proposes refineR, an indirect method for estimating reference intervals from real‑world data, avoiding the need for healthy samples. RefineR separates non‑pathological from pathological test results via an inverse approach, selects the best‑fitting model, and is benchmarked against ground truth, the kosmic indirect method, and direct sampling. RefineR achieved a 2.77% mean error, outperformed kosmic and small‑sample direct methods, matched direct methods with large samples, and produced pediatric intervals comparable to published studies, demonstrating precise, real‑world reference interval estimation.

Abstract

Reference intervals are essential for the interpretation of laboratory test results in medicine. We propose a novel indirect approach to estimate reference intervals from real-world data as an alternative to direct methods, which require samples from healthy individuals. The presented refineR algorithm separates the non-pathological distribution from the pathological distribution of observed test results using an inverse approach and identifies the model that best explains the non-pathological distribution. To evaluate its performance, we simulated test results from six common laboratory analytes with a varying location and fraction of pathological test results. Estimated reference intervals were compared to the ground truth, an alternative indirect method (kosmic), and the direct method (N = 120 and N = 400 samples). Overall, refineR achieved the lowest mean percentage error of all methods (2.77%). Analyzing the amount of reference intervals within ± 1 total error deviation from the ground truth, refineR (82.5%) was inferior to the direct method with N = 400 samples (90.1%), but outperformed kosmic (70.8%) and the direct method with N = 120 (67.4%). Additionally, reference intervals estimated from pediatric data were comparable to published direct method studies. In conclusion, the refineR algorithm enables precise estimation of reference intervals from real-world data and represents a viable complement to the direct method.